docs: def random_subset(self, relative_size, balance_labels=False, label_list_ids=None):
          """
          Create a subview of random utterances with a approximate size relative to the full corpus.
          By default x random utterances are selected with x equal to ``relative_size * corpus.num_utterances``.
  
          Args:
              relative_size (float): A value between 0 and 1.
                                     (0.5 will create a subset with approximately 50% of the full corpus size)
              balance_labels (bool): If True, the labels of the selected utterances are balanced as far as possible.
                                     So the count/duration of every label within the subset is equal.
              label_list_ids (list): List of label-list ids. If none is given, all label-lists are considered
                                     for balancing. Otherwise only the ones that are in the list are considered.
  
          Returns:
              Subview: The subview representing the subset.
          """
  
          num_utterances_in_subset = round(relative_size * self.corpus.num_utterances)
          all_utterance_ids = sorted(list(self.corpus.utterances.keys()))
  
          if balance_labels:
              all_label_values = self.corpus.all_label_values(label_list_ids=label_list_ids)
              utterance_with_label_counts = collections.defaultdict(dict)
  
              for utterance_idx, utterance in self.corpus.utterances.items():
                  utterance_with_label_counts[utterance_idx] = utterance.label_count(label_list_ids=label_list_ids)
  
              subset_utterance_ids = utils.select_balanced_subset(utterance_with_label_counts,
                                                                  num_utterances_in_subset,
                                                                  list(all_label_values),
                                                                  seed=self.rand.random())
  
          else:
              subset_utterance_ids = self.rand.sample(all_utterance_ids,
                                                      num_utterances_in_subset)
  
          filter = subview.MatchingUtteranceIdxFilter(utterance_idxs=set(subset_utterance_ids))
          return subview.Subview(self.corpus, filter_criteria=[filter])